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Publications in Math-Net.Ru |
Citations |
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2023 |
| 1. |
G. B. Marshalko, R. A. Romanenkov, J. A. Trufanova, “Security analysis of the draft national standard «Neural network algorithms in protected execution. Automatic training of neural network models on small samples in classification tasks»”, Proceedings of ISP RAS, 35:6 (2023), 179–188  |
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2022 |
| 2. |
G. B. Marshalko, J. A. Trufanova, “Polynomial approximations for several neural network activation functions”, Informatics and Automation, 21:1 (2022), 161–180 |
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2020 |
| 3. |
G. B. Marshalko, V. I. Rudskoy, “Key distribution. Episode 1: Quantum menace”, Mat. Vopr. Kriptogr., 11:2 (2020), 99–110 |
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2015 |
| 4. |
D. M. Dygin, I. V. Lavrikov, G. B. Marshalko, V. I. Rudskoy, D. I. Trifonov, V. A. Shishkin, “On a new Russian Encryption Standard”, Mat. Vopr. Kriptogr., 6:2 (2015), 29–34   |
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2014 |
| 5. |
G. B. Marshalko, “On the security of a neural network-based biometric authentication scheme”, Mat. Vopr. Kriptogr., 5:2 (2014), 87–98 |
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| 6. |
A. A. Dmukh, D. M. Dygin, G. B. Marshalko, “A lightweight-friendly modification of GOST block cipher”, Mat. Vopr. Kriptogr., 5:2 (2014), 47–55 |
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2011 |
| 7. |
A. S. Kuzmin, G. B. Marshalko, “Reconstruction of linear recurrent sequence over prime residue ring from its image. II”, Mat. Vopr. Kriptogr., 2:2 (2011), 81–93 |
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2010 |
| 8. |
A. S. Kuz'min, G. B. Marshalko, A. A. Nechaev, “Reconstruction of linear recurrent sequence over prime residue ring from its image”, Mat. Vopr. Kriptogr., 1:2 (2010), 31–56 |
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| Presentations in Math-Net.Ru |
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